A method of integrating spatial proteomics and protein-protein interaction network data
A method of integrating spatial proteomics and protein-protein interaction network data
The increase in quantity of spatial proteomics data requires a range of analytical techniques to effectively analyse the data. We provide a method of integrating spatial proteomics data together with protein-protein interaction (PPI) networks to enable the extraction of more information. A strong relationship between spatial proteomics and PPI network data was demonstrated. Then a method of converting the PPI network into vectors using spatial proteomics data was explained which allows the integration of the two datasets. The resulting vectors were tested using machine learning techniques and reasonable predictive accuracy was found.
Bioinformatics, Spatial proteomics, Machine learning
Squires, Steven, Edward
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Ewing, Robert
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Prugel-Bennett, Adam
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Niranjan, Mahesan
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Squires, Steven, Edward
68512c11-065d-45e7-a0a9-54a32198e6b3
Ewing, Robert
022c5b04-da20-4e55-8088-44d0dc9935ae
Prugel-Bennett, Adam
b107a151-1751-4d8b-b8db-2c395ac4e14e
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Squires, Steven, Edward, Ewing, Robert, Prugel-Bennett, Adam and Niranjan, Mahesan
(2017)
A method of integrating spatial proteomics and protein-protein interaction network data.
In Lecture Notes in Computer Science.
Springer.
9 pp
.
(doi:10.1007/978-3-319-70139-4_79).
Record type:
Conference or Workshop Item
(Paper)
Abstract
The increase in quantity of spatial proteomics data requires a range of analytical techniques to effectively analyse the data. We provide a method of integrating spatial proteomics data together with protein-protein interaction (PPI) networks to enable the extraction of more information. A strong relationship between spatial proteomics and PPI network data was demonstrated. Then a method of converting the PPI network into vectors using spatial proteomics data was explained which allows the integration of the two datasets. The resulting vectors were tested using machine learning techniques and reasonable predictive accuracy was found.
Text
Proteomics
- Accepted Manuscript
More information
Accepted/In Press date: 31 July 2017
e-pub ahead of print date: 29 October 2017
Keywords:
Bioinformatics, Spatial proteomics, Machine learning
Identifiers
Local EPrints ID: 413521
URI: http://eprints.soton.ac.uk/id/eprint/413521
ISSN: 0302-9743
PURE UUID: a60d41bb-5a00-43b4-9ecc-9bc275feb4d8
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Date deposited: 25 Aug 2017 16:31
Last modified: 16 Mar 2024 05:40
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Contributors
Author:
Steven, Edward Squires
Author:
Adam Prugel-Bennett
Author:
Mahesan Niranjan
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